Physics-informed autoregressive neural processes for wear degradation prognostics with application to magnetic head

Wear-induced degradation is an unavoidable and widespread failure mechanism that significantly impacts the performance of components, especially in microelectromechanical systems (MEMS) like hard disk drives (HDDs). In industry, there is a concurrent desire not only to estimate degradation but also to obtain information regarding the uncertainty associated with these predictions. Neural Processes (NPs) combine neural networks with Gaussian process properties to produce predictions with quantified uncertainty; however, conventional NPs overlook temporal dependencies in wear sequences, limiting their ability to model sequential correlations. To address this, we propose a Physics-Informed Autoregressive Neural Processes (PIARNP) for wear degradation prediction in HDDs. PIARNP captures temporal wear dynamics by embedding a Gated Recurrent Unit (GRU)-based autoregressive mechanism within the NPs framework, forming the Autoregressive Neural Processes (ARNP). Building on ARNP, it integrates two modules derived from Barwell’s degradation equation as physics-informed priors, guiding the model to learn latent representations that are physically consistent. In addition, we introduce time-weighted performance metrics to evaluate the model’s performance from an engineering perspective. Evaluation results show that the proposed model achieves superior performance under multiple metrics, confirming its effectiveness in wear prediction. Furthermore, the distribution of the probabilistic integral transform (PIT) demonstrates the reliability of the proposed model.